Hoe kunnen we de Afrikaanse maniok boer het best helpen?

Audrey
Vanderghinste

Al jaren breken wetenschappers, politiekers, en ondernemers hun hoofd over hoe we Afrikaanse boeren kunnen helpen meer te produceren. Dit zou namelijk ten goede komen voor het hele continent, en zelfs voor de hele wereld. Eén van de methodes die ingezet werden, zijn zogenaamde "extension services", of voorlichtingsdiensten. Hierbij worden boeren op de hoogte gesteld van nieuwe technologieën die hun oogst doen stijgen. Men dacht dus dat het grootste probleem gewoon een tekort aan informatie was. We verwachtten grote successen, maar helaas bleven die uit. Tijdens het onderzoek naar de oorzaak van de uitgebleven positieve resultaten, besefte men dat de meeste boeren de overgebrachte informatie gewoon negeerde. Het begon te dagen dat een "one-size-fits-all" methode gedoemd was om te mislukken. Je kan namelijk niet hetzelfde advies geven aan alle boeren die zo veel verschillen in hun financiële, geografische, of sociale situatie. Men begreep dat het advies aangepast moest worden aan elke boer zijn/haar persoonlijke situatie.



De African Cassava Agronomy Initiative, of kortweg ACAI, heeft onderzocht welke nieuwe technieken en technologieën de oogst van maniok (in het Engels: cassava) kan verhogen. Aangezien maniok één van de belangrijkste gewassen is in Afrika, kunnen hun bevindingen de Afrikaanse boeren enorm helpen. Maar ACAI besefte dus dat ze hun advies telkens een beetje moesten aanpassen aan elke boer voor optimale toepassing van hun bevindingen. Deze scriptie probeert een methode te vinden voor gemakkelijkere personalisatie van advies, met behulp van keuze-experimenten. Keuze-experimenten helpen de voorkeuren van boeren omtrent de karakteristieken van de te verspreiden technologieën in te schatten. In het geval van ACAI beseften we dat de voorkeur van de boer omtrent zes karakteristieken een invloed kon hebben op hun implementatie. In totaal hebben we 333 boeren geïnterviewd in Tanzania. Dan hebben we onderzocht of er een relatie te vinden was tussen de boeren hun voorkeur en hun socio-economische situatie. Het doel is dus om te weten of en hoe advies aangepast moet worden aan boeren met een verschillende socio-economische achtergrond om de implementatie te verhogen.

De resultaten van de analyse tonen dat advies om verspreid te planten en te oogsten geen personalisatie hoeft, maar een grotere implementatie wordt wel bij de welvarendere boeren verwacht. Voor kunstmestgebruik ligt de grootste aversie bij de boeren met het kleinste maniok veld en de minste ervaring met kunstmestgebruik voor maniok. De minst welvarende boeren tonen aan open te staan voor kunstmestgebruik. Er wordt echter voorspeld dat de implementatie bij hun ook laag zal zijn, tenzij ze toegang hebben tot krediet. Gezien dat ook een gegarandeerde afzetmarkt het meest in smaak valt bij deze minst welvarende klasse, worden beleidsvormers en grote cassave kopers aangeraden om de mogelijkheid van contracten gecombineerd met krediettoegang te analyseren. Zo'n contracten tussen boeren en kopers zou de minst welvarende families een veel betere financiële situatie geven.

Bibliografie

ACAI. (2015). African Cassava Agronomy Initiative (ACAI). Retrieved April 24, 2019, from https://www.iita.org/iita-project/acai-african-cassava-agronomy-initiat…

Adam, C., Bevan, D., & Gollin, D. (2018). Rural–Urban Linkages, Public Investment and Transport Costs: The Case of Tanzania. World Development, 109, 497–510. https://doi.org/10.1016/J.WORLDDEV.2016.08.013

Arndt, C., Farmer, W., Strzepek, K., & Thurlow, J. (2012). Climate Change, Agriculture and Food Security in Tanzania. Review of Development Economics, 16(3), 378–393. https://doi.org/10.1111/j.1467-9361.2012.00669.x

Asrat, S., Yesuf, M., Carlsson, F., & Wale, E. (2010). Farmers’ preferences for crop variety traits: Lessons for on-farm conservation and technology adoption. Ecological Economics, 69(12), 2394–2401. https://doi.org/10.1016/J.ECOLECON.2010.07.006

Audibert, M., He, Y., & Mathonnat, J. (2013). Multinomial and Mixed Logit Modeling in the Presence of Heterogeneity: A Two-Period Comparison of Healthcare Provider Choice in Rural China. Retrieved from www.cerdi.org

Awotide, B. A., Alene, A. D., Abdoulaye, T., & Manyong, V. M. (2015). Impact of agricultural technology adoption on asset ownership: the case of improved cassava varieties in Nigeria. Food Security, 7(6), 1239–1258. https://doi.org/10.1007/s12571- 015-0500-7

Ayenew, H. Y. (2016). Production Efficiency and Market Orientation in Food Crops in North West Ethiopia: Application of Matching Technique for Impact Assessment. PloS One, 11(7), e0158454. https://doi.org/10.1371/journal.pone.0158454

Bationo, A., Ngaradoum, D., Youl, S., Lompo, F., & Fening, J. O. (Eds.). (2018). Improving the Profitability, Sustainability and Efficiency of Nutrients Through Site Specific Fertilizer Recommendations in West Africa Agro-Ecosystems. https://doi.org/10.1007/978-3-319-58789-9

Bennett, J., & Birol, E. (2010). Choice experiments in developing countries : implementation, challenges and policy implications. Retrieved from https://search.proquest.com/docview/761671750?OpenUrlRefId=info:xri/sid… ountid=17215

Boxall, P. C., & Adamowicz, W. L. (2002). Understanding Heterogeneous Preferences in Random Utility Models: A Latent Class Approach. In Environmental and Resource Economics (Vol. 23). Retrieved from https://link-springer-

com.kuleuven.ezproxy.kuleuven.be/content/pdf/10.1023%2FA%3A1021351721619.pdf Bradley, M., & Daly, A. (1994). Use of the logit scaling approach to test for rank-order and

fatigue effects in stated preference data. Transportation, 21(2), 167–184.

https://doi.org/10.1007/BF01098791

Brownstone, D., & Small, K. A. (2005). Valuing time and reliability: assessing the evidence

from road pricing demonstrations. Transportation Research Part A: Policy and

Practice, 39(4), 279–293. https://doi.org/10.1016/j.tra.2004.11.001

Cai, Y., Golub, A. A., & Hertel, T. W. (2017). Agricultural research spending must increase

in light of future uncertainties. Food Policy, 70, 71–83.

https://doi.org/10.1016/J.FOODPOL.2017.06.002

Carter, M. R. (2016). What farmers want: the “gustibus multiplier” and other behavioral

insights on agricultural development. Agricultural Economics, 47(S1), 85–96.

https://doi.org/10.1111/agec.12312

Cascetta, E. (2009). TRANSPORTATION SYSTEMS ANALYSIS. Retrieved from https://link-

springer-com.kuleuven.ezproxy.kuleuven.be/content/pdf/10.1007%2F978-0-387-75857-

2.pdf

Caussade, S., Ortúzar, J. de D., Rizzi, L. I., & Hensher, D. A. (2005). Assessing the influence

of design dimensions on stated choice experiment estimates. Transportation Research

Part B: Methodological, 39(7), 621–640. https://doi.org/10.1016/J.TRB.2004.07.006 CGIAR. (2019). CGIAR: Science for humanity’s greatest challenges. Retrieved March 15,

2019, from https://www.cgiar.org/

ChoiceMetrics. (2018). Ngene 1.2 USER MANUAL & REFERENCE GUIDE The

Cutting Edge in Experimental Design. Retrieved from www.choice-metrics.com Dalemans, F., Muys, B., & Maertens, M. (2019). Adoption Constraints for Small-scale Agroforestry-based Biofuel Systems in India. Ecological Economics, 157, 27–39.

https://doi.org/10.1016/J.ECOLECON.2018.10.020

Dercon, S., & Christiaensen, L. (2011). Consumption risk, technology adoption and poverty

traps: Evidence from Ethiopia. Journal of Development Economics, 96(2), 159–173.

https://doi.org/10.1016/J.JDEVECO.2010.08.003

Dinar, A., Karagiannis, G., & Tzouvelekas, V. (2007). Evaluating the impact of agricultural

extension on farms’ performance in Crete: a nonneutral stochastic frontier approach. Agricultural Economics, 36(2), 135–146. https://doi.org/10.1111/j.1574- 0862.2007.00193.x

Duquette, E., Higgins, N., & Horowitz, J. (2013). Time Preference and Technology X

Adoption: A Single-Choice Experiment with U.S. Farmers. 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. Retrieved from https://ideas.repec.org/p/ags/aaea13/150719.html

Eicher, C. K. (1990). Building African scientific capacity for agricultural development. Agricultural Economics, 4(2), 117–143. https://doi.org/10.1016/0169-5150(90)90028-Y

Ellsberg, D. (1961). Risk, Ambiguity, and the Savage Axioms. The Quarterly Journal of Economics, 75(4), 643. https://doi.org/10.2307/1884324

Emmanuel, D., Owusu-Sekyere, E., Owusu, V., & Jordaan, H. (2016). Impact of agricultural extension service on adoption of chemical fertilizer: Implications for rice productivity and development in Ghana. NJAS - Wageningen Journal of Life Sciences, 79, 41–49. https://doi.org/10.1016/J.NJAS.2016.10.002

Evenson, R. E., & Gollin, D. (2003). Assessing the impact of the green revolution, 1960 to 2000. National Library of Medicine, 300(5620), 758–762. Retrieved from https://search- proquest- com.kuleuven.ezproxy.kuleuven.be/docview/1875859882?rfr_id=info%3Axri%2Fsid% 3Aprimo

FAO, & IFAD. (2005). A review of cassava in Africa with country case studies on Nigeria, Ghana, the United Republic of Tanzania, Uganda and Benin. Retrieved from http://www.fao.org/3/a-a0154e.pdf

FAOSTAT. (2017). FAOSTAT. Retrieved April 22, 2019, from http://www.fao.org/faostat/en/#data/QC

Fermont, A. M., van Asten, P. J. A., Tittonell, P., van Wijk, M. T., & Giller, K. E. (2009). Closing the cassava yield gap: An analysis from smallholder farms in East Africa. Field Crops Research, 112(1), 24–36. https://doi.org/10.1016/J.FCR.2009.01.009

Gangwar, L. S., Saran, S., & Kumar, S. (2013). Impact of Public Sector Research and Extension on Backyard Poultry Production in Kumaon Hills - An Economic Analysis. Retrieved April 18, 2019, from https://search-proquest- com.kuleuven.ezproxy.kuleuven.be/docview/1475042943?rfr_id=info%3Axri%2Fsid% 3Aprimo

Gómez, W., Salgado, H., Vásquez, F., & Chávez, C. (2014). Using stated preference methods to design cost-effective subsidy programs to induce technology adoption: An application to a stove program in southern Chile. Journal of Environmental Management, 132, 346– 357. https://doi.org/10.1016/J.JENVMAN.2013.11.020

Haji, J. (2007). Production Efficiency of Smallholders’ Vegetable-dominated Mixed Farming XI

System in Eastern Ethiopia: A Non-Parametric Approach. Journal of African

Economies, 16(1), 1–27. https://doi.org/10.1093/jae/ejl044

Hillocks, R. J. (2014). Addressing the Yield Gap in Sub-Saharan Africa. Outlook on

Agriculture, 43(2), 85–90. https://doi.org/10.5367/oa.2014.0163

Howeler, R., Lutaladio, N., & Thomas, G. (2013). Save and grow: Cassava. Retrieved from

http://www.fao.org/3/a-i3278e.pdf

Hu, & Wuyang. (2006). Effects of Endogenous Task Complexity and the Endowed Bundle

on Stated Choice. 2006 Annual Meeting, July 23-26, Long Beach, CA. Retrieved from

https://ideas.repec.org/p/ags/aaea06/21437.html

IITA. (2014). Grant Proposal Narrative ACAI.

Jarvis, A., Ramirez-Villegas, J., Herrera Campo, B. V., & Navarro-Racines, C. (2012). Is

Cassava the Answer to African Climate Change Adaptation?

https://doi.org/10.1007/s12042-012-9096-7

Kalirajan, K. P., & Shand, R. T. (1988). Firm and product-specific technical efficiencies in a

multiproduct cycle system. The Journal of Development Studies, 25(1), 83–96.

https://doi.org/10.1080/00220388808422096

Kassahun, H. T., & Jacobsen, J. B. (2015). Economic and institutional incentives for

managing the Ethiopian highlands of the Upper Blue Nile Basin: A latent class analysis.

Land Use Policy, 44, 76–89. https://doi.org/10.1016/j.landusepol.2014.11.017

Khan, M. H., & Akbari, A. H. (1986). Impact of Agricultural Research and Extension on crop

productivity in Pakistan: A production function approach. World Development, 14(6),

757–762. https://doi.org/10.1016/0305-750X(86)90017-3

Khandker, S., & Mahmud, W. (2012). Seasonal Hunger and Public Policies: Evidence from

Northwest Bangladesh - Shahidur R. Khandker, Wahiduddin Mahmud - Google Books. Retrieved from https://books.google.be/books?id=8MK3ka6UuhkC&pg=PA17&lpg=PA17&dq=hunger +crop+cassava&source=bl&ots=lC8cVKczAG&sig=ACfU3U2h1P2Lza8WCsCH8Ule_ - 5ztRYqXw&hl=en&sa=X&ved=2ahUKEwjq05e2y4ThAhXlXhUIHUY9ChEQ6AEwF XoECAkQAQ#v=onepage&q=famine reserve&f=false

Kidanemariam, G. (2017). South African journal of economic and management sciences. In South African Journal of Economic and Management Sciences (Vol. 20). Retrieved from https://sajems.org/index.php/sajems/article/view/1349/849

Krishnan, P., & Patnam, M. (2014). Neighbors and Extension Agents in Ethiopia: Who XII

Matters More for Technology Adoption? American Journal of Agricultural Economics,

96(1), 308–327. https://doi.org/10.1093/ajae/aat017

Lambrecht, I., Vanlauwe, B., & Maertens, M. (2016a). Agricultural extension in Eastern

Democratic Republic of Congo: does gender matter? European Review of Agricultural

Economics, 43(5), 841–874. https://doi.org/10.1093/erae/jbv039

Lambrecht, I., Vanlauwe, B., & Maertens, M. (2016b). Integrated soil fertility management:

from concept to practice in Eastern DR Congo. International Journal of Agricultural

Sustainability, 14(1), 100–118. https://doi.org/10.1080/14735903.2015.1026047 Lambrecht, I., Vanlauwe, B., Merckx, R., & Maertens, M. (2014). Understanding the Process

of Agricultural Technology Adoption: Mineral Fertilizer in Eastern DR Congo. World

Development, 59, 132–146. https://doi.org/10.1016/J.WORLDDEV.2014.01.024 Lambrecht, I., Vranken, L., Merckx, R., Vanlauwe, B., & Maertens, M. (2015). Ex Ante

Appraisal of Agricultural Research and Extension. Outlook on Agriculture, 44(1), 61–

67. https://doi.org/10.5367/oa.2015.0199

Liverpool, L. S. O., & Winter-Nelson, A. (2010). Poverty Status and the Impact of Formal

Credit on Technology Use and Wellbeing among Ethiopian Smallholders. World

Development, 38(4), 541–554. https://doi.org/10.1016/J.WORLDDEV.2009.11.006 Maertens, A., & Barrett, C. B. (2012). Measuring Social Networks’ Effects on Agricultural Technology Adoption. American Journal of Agricultural Economics, 95(2), 353–359.

https://doi.org/10.1093/ajae/aas049

Mathijs, E., & Vranken, L. (2010). Post-Communist Economies Human Capital, Gender and

Organisation in Transition Agriculture: Measuring and Explaining the Technical Efficiency of Bulgarian and Hungarian Farms. https://doi.org/10.1080/14631370120052654

McSweeney, C., New, M., & Lizcano, G. (2010). UNDP Climate Change Country Profiles: Tanzania | Climate and Development Learning Platform. Retrieved from https://www.climatelearningplatform.org/undp-climate-change-country-pro…- tanzania

Mooney, D. F., Assistant, R., Student, P., & Barham, B. L. (2013). What Drives the Adoption of Clean Agricultural Technologies? An Ex Ante Assessment of Sustainable Biofuel Production in Southwestern Wisconsin. Retrieved from https://ageconsearch.umn.edu/record/150557/files/AAEA - Clean technology - 09- 19.pdf

Nakano, Y., Kajisa, K., & Otsuka, K. (2015). On the Possibility of Rice Green Revolution in XIII

Irrigated and Rainfed Areas in Tanzania: An Assessment of Management Training and Credit Programs. Retrieved from https://ageconsearch.umn.edu/record/212036/files/Tanzania rice technology1.pdf

Nakano, Y., Tsusaka, T. W., Aida, T., & Pede, V. O. (2018). Is farmer-to-farmer extension effective? The impact of training on technology adoption and rice farming productivity in Tanzania. World Development, 105, 336–351. https://doi.org/10.1016/J.WORLDDEV.2017.12.013

National Agricultural and Forestry Extension Service (NAFES). (2005). Consolidating Extension in the Lao PDR. Retrieved from http://www.laolink.org/Literature/Consolidating_Extension_Laos.pdf

Ngailo, S., Shimelis, H. A., Sibiya, J., & Mtunda, K. (2016). Assessment of sweetpotato farming systems, production constraints and breeding priorities in eastern Tanzania. South African Journal of Plant and Soil, 33(2), 105–112. https://doi.org/10.1080/02571862.2015.1079933

Oakley, P., & Garforth, C. (1985). Guide to extension training. Retrieved from http://www.fao.org/3/a-t0060e.pdf

Obisesan, A. (2014). Gender Differences In Technology Adoption And Welfare Impact Among Nigerian Farming Households. In IDEAS Working Paper Series from RePEc; St. Louis. Retrieved from https://search-proquest- com.kuleuven.ezproxy.kuleuven.be/docview/1700391295?rfr_id=info%3Axri%2Fsid% 3Aprimo

Obisesan, A. A., Amos, T. T., & Akinlade, R. J. (2016). CAUSAL EFFECT OF CREDIT AND TECHNOLOGY ADOPTION ON FARM OUTPUT AND INCOME: THE CASE OF CASSAVA FARMERS IN SOUTHWEST NIGERIA. Retrieved from https://ageconsearch.umn.edu/record/246443/files/182. Causal effect of credit and technology adoption in Nigeria.pdf

Otsuka, K., & Larson, D. F. (2013). An African Green Revolution: Finding Ways to Boost Productivity on Small Farms. Retrieved from https://link-springer- com.kuleuven.ezproxy.kuleuven.be/content/pdf/10.1007%2F978-94-007-5760-8.pdf

Owens, T., & Hoddinott, J. (2001). The impact of agricultural extension on farm production in resettlement areas of Zimbabwe. Retrieved April 18, 2019, from https://search- proquest- com.kuleuven.ezproxy.kuleuven.be/docview/1698920961?rfr_id=info%3Axri%2Fsid% 3Aprimo

XIV

Parmar, A., Sturm, B., & Hensel, O. (2017). Crops that feed the world: Production and improvement of cassava for food, feed, and industrial uses. https://doi.org/10.1007/s12571-017-0717-8

Sanga, C., Kalungwizi, V., & Msuya, C. (2013). Building an agricultural extension services system supported by ICTs in Tanzania: Progress made, Challenges remain. International Journal of Education and Development Using Information and Communication Technology, 9(1), 80–99. Retrieved from https://search-proquest- com.kuleuven.ezproxy.kuleuven.be/docview/1353085132?rfr_id=info%3Axri%2Fsid% 3Aprimo

Sewando, P. T. (2014). Development in Practice Cassava value chain and its products in Morogoro rural district, Tanzania. https://doi.org/10.1080/09614524.2014.966653 Skreli, E., Imami, D., & Zvyagintsev, D. (2014). Government Extension Service Impact

Assessment. Retrieved from

https://ageconsearch.umn.edu/record/169394/files/paper_Skreli_Imami_Zvy… Stata. (2019). Factor analysis. Retrieved from

https://www.stata.com/manuals13/mvfactor.pdf

Suvedi, M., Ghimire, R., & Kaplowitz, M. (2017). Farmers’ participation in extension

programs and technology adoption in rural Nepal: a logistic regression analysis. The Journal of Agricultural Education and Extension, 23(4), 351–371. https://doi.org/10.1080/1389224X.2017.1323653

Uchechukwu-Agua, A. D., Caleb, O. J., & Linus Opara, U. (2015). Postharvest Handling and Storage of Fresh Cassava Root and Products: a Review. https://doi.org/10.1007/s11947- 015-1478-z

UNESCO UIS. (2015). United Republic of Tanzania. Retrieved April 16, 2019, from http://uis.unesco.org/country/TZ

World Bank. (1999). Agricultural Extension_ The Kenya Experience. Retrieved from http://documents.worldbank.org/curated/en/972111468758711518/pdf/multi0…

Wossen, T., Alene, A., Abdoulaye, T., Feleke, S., Rabbi, I. Y., & Manyong, V. (2018). Poverty Reduction Effects of Agricultural Technology Adoption: The Case of Improved Cassava Varieties in Nigeria. Journal of Agricultural Economics. https://doi.org/10.1111/1477-9552.12296

Young, D., & Deng, H. (1999). The effects of education in early-stage agriculture: some evidence from China. https://doi.org/10.1080/000368499323193

Download scriptie (4.39 MB)
Universiteit of Hogeschool
KU Leuven
Thesis jaar
2019
Promotor(en)
Miet Maertens & Roel Merckx